Background

Plasma extracellular vesicles (EVs), especially exosome-like vesicles (ELVs), are being increasingly explored as a source of potential noninvasive disease biomarkers. The discovery of blood-based biomarkers associated with ELVs requires methods that isolate high yields of these EVs without significant contamination with highly abundant plasma proteins and lipoproteins. The rising interest in blood-based EV-associated biomarkers has led to the rapid development of novel EV isolation methods. However, the field suffers from a lack of standardization and often, new techniques are used without critical evaluation. Size exclusion chromatography (SEC) has become the method of choice for rapid isolation of relatively pure EVs from plasma, yet it has technical limitations for certain downstream applications. The recently released exoEasy kit (Qiagen) is a new membrane affinity spin column method for the isolation of highly pure EVs from biofluids with the potential to overcome most of the limitations of SEC.

Methods

By using multiple complementary techniques we assessed the performance of the exoEasy kit in isolating ELVs from 2 ml of human plasma and compared it with the SEC qEV column (Izon Science).

Results

Our data show that exoEasy kit isolates a heterogenous mixture of particles with a larger median diameter, broader size range and a higher yield than the SEC qEV column. The exclusive presence of small RNAs in the particles and the total RNA yield were comparable to the SEC qEV column. Despite being less prone to low density lipoprotein contamination than the SEC qEV column, the overall purity of exoEasy kit EV preparations was suboptimal. The low particle-protein ratio, significant amount of albumin, very low levels of exosome-associated proteins and propensity to triglyceride-rich lipoprotein contamination suggest isolation of mainly non-ELVs and co-isolation of plasma proteins and certain lipoproteins by the exoEasy kit.

Conclusions

We demonstrate that performance of exoEasy kit for the isolation of ELVs for biomarker discovery is inferior to the SEC qEV column. This comprehensive evaluation of a novel EV isolation method contributes to the acceleration of the discovery of EV-associated biomarkers and the development of EV-based diagnostics.